Instructions to use shibing624/text2vec-base-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use shibing624/text2vec-base-chinese with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("shibing624/text2vec-base-chinese") sentences = [ "那是 個快樂的人", "那是 條快樂的狗", "那是 個非常幸福的人", "今天是晴天" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Potential Inconsistencies Model and Datasets License
Hi, while reviewing the licenses for this model and datasets it depends on, I noticed a potential inconsistency that could cause confusion or legal risks in some situations.
Your model utilizes the dataset shibing624/nli_zh under the cc-by-4.0. However, the license of your model is apache-2.0, i.e., less strict than cc-by-4.0 on license terms, such as sublicense, which may impact the whole license compatibility in your repository, thus confusing subsequent users and bringing possible legal and financial risks.
If possible, you can fix them in one of the following ways:
1.It could be helpful to select another proper license for your repository.
2.You may want to gently remind users that, in some cases, they should check both the model license and the base model license, especially when redistributing or modifying the model.
use apache-2.0, i will update shibing624/nli_zh to apache 2.0.